Qwen3 0.6B SFT Name Parser Yaml

Name: Qwen3 0.6B SFT Name Parser Yaml
Author: small-models-for-glam

233

600M

4 languages

license:apache-2.0

small-models-for-glam

Language Model

OTHER

0.6B params

New

233 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

This model is a fine-tuned version of Qwen/Qwen3-0.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

Quick Startpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "small-models-for-glam/Qwen3-0.6B-SFT-name-parser-yaml"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Parse a person name
input_name = "Dr. Jane Smith-Jones, 1850-1920"
prompt = "Parse this person name:\n\n" + input_name

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content if present
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # </think> token
except ValueError:
    index = 0

content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
print(content)

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