sf-100/README.md

tags, license, language

tags	license	language
text-generation-inference transformers qwen2	apache-2.0	en

sf-100

Conversational fine-tune of Qwen2.5-7B-Instruct, supervised-fine-tuned Hugging Face TRL.

Model details

• Architecture: Qwen2 (7B)
• Parameters: ~7.6B (reported as 8B in repo metadata)
• Precision: BF16 merged weights, trained on top of a 4-bit bnb-quantized base
• License: Apache-2.0
• Language: Multi
• Developed by: liamka

Intended use

General-purpose conversational assistant — single- and multi-turn chat.

Not suitable for safety-critical settings (medical, legal, financial advice), non-English input (not evaluated), or high-stakes factual lookup without verification.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "liamka/sf-100"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Hi, who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Training

• Framework: Unsloth + TRL (SFTTrainer)
• Method: Supervised fine-tuning on top of Qwen2.5-7B-Instruct
• No RLHF / DPO applied

Dataset, step count and hyperparameters are not published.

Limitations

• Inherits biases and knowledge cutoff from Qwen2.5-7B-Instruct.
• SFT only — no preference optimisation, so safety and refusal behaviour matches the base or weaker.
• Can hallucinate. Verify factual claims.
• Evaluated only informally; no benchmark numbers reported.

Acknowledgements

• Qwen2.5 — Alibaba
• TRL — Hugging Face