HydraCoder

16
5
30.0B
1 language
license:apache-2.0
by
Daemontatox
Language Model
OTHER
30B params
New
16 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
68GB+ RAM
Mobile
Laptop
Server
Quick Summary

HydraCoder is a state-of-the-art Rust-specialized coding model built on Qwen/Qwen3-Coder-30B-A3B-Instruct, designed for high-fidelity, idiomatic Rust code generation, completion, and repair.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
28GB+ RAM

Code Examples

lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
lora dropout = 0.01text
You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
    Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
    Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices. 
    Ask for clarification if the input is ambiguous. 
    Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.


    Approach this like an intermediate-level Rust and LLM engineer.
    Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
    Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
    Avoid overconfidence.
    Explain your reasoning clearly, even if the final answer is simple.
    prompt:
    {}

    Reasoning:
    {}

    response:
    {}
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)

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