Gpt Oss Coder 20b

311
8
20.0B
1 language
license:apache-2.0
by
yasserrmd
Language Model
OTHER
20B params
New
311 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
45GB+ RAM
Mobile
Laptop
Server
Quick Summary

This model is a fine-tuned version of OpenAI's GPT-OSS-20B, optimized for code generation tasks.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
19GB+ RAM

Code Examples

Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    reasoning_effort="low",
).to(model.device)

streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)

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