Instructions to use ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning")
model = AutoModelForCausalLM.from_pretrained("ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning

SGLang

How to use ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning with Docker Model Runner:
```
docker model run hf.co/ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning

Fine-tuned to reproduce Claude's chain-of-thought reasoning traces with <think> blocks for transparent, step-by-step problem solving.

Fine-tuned from Qwen/Qwen3.5-0.8B on Claude Opus Reasoning Distillation using TRL with LoRA adapters.

Overview

Property	Value
Developed by	Behrooz Azarkhalili
License	APACHE-2.0
Language	English
Base Model	Qwen/Qwen3.5-0.8B
Model Size	0.8B parameters
Training Method	SFT with LoRA (TRL)
Dataset	Claude Opus Reasoning Distillation (~10,477 samples)
Context Length	2,048 tokens
Hardware	NVIDIA H100 80GB HBM3 (MIG 3g.40gb)
Cluster	DRAC / Fir (Compute Canada)

Training Configuration

Parameter	Value
Framework	TRL
Learning Rate	2e-4
Batch Size	2 per device
Gradient Accumulation	8
Effective Batch Size	16
Training	1 epoch(s)
Max Sequence Length	2,048 tokens
Precision	BF16 mixed precision
Gradient Checkpointing	Enabled

LoRA Configuration

Parameter	Value
LoRA Rank (r)	64
LoRA Alpha	128
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Quantization	4-bit NF4 (QLoRA)

Usage

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain step by step how to solve 2x + 5 = 13."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Pipeline

from transformers import pipeline
generator = pipeline("text-generation", model="ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning", device_map="auto")
output = generator([{"role": "user", "content": "What is 2+2?"}], max_new_tokens=256, return_full_text=False)
print(output[0]["generated_text"])

4-bit Quantized

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch
model = AutoModelForCausalLM.from_pretrained(
    "ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16),
    device_map="auto"
)

GGUF Versions

Quantized GGUF versions for CPU/edge inference: ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning-GGUF

Quantization	Bits	Use Case
Q2_K	2	Edge devices, mobile
Q3_K_M	3	Constrained environments
Q4_K_M	4	Best quality/size balance (recommended)
Q5_K_M	5	Higher quality
Q6_K	6	Near-lossless
Q8_0	8	Maximum quality

Ollama

ollama pull hf.co/ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning-GGUF:Q4_K_M
ollama run hf.co/ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning-GGUF:Q4_K_M "Hello!"

llama.cpp

llama-cli -m Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning-Q4_K_M.gguf -p "Your prompt here" -n 256

Limitations

Language: Primarily trained on English data
Knowledge Cutoff: Limited to base model's training data cutoff
Hallucinations: May generate plausible-sounding but incorrect information
Context Length: Fine-tuned with 2,048 token limit
Safety: Not extensively safety-tuned; use with appropriate guardrails

Intended Use

Research on language model fine-tuning and reasoning distillation
Educational purposes and experimentation
Prototyping conversational AI and tool-use agents
Not recommended for production without additional safety measures

Framework Versions

TRL: 1.0.0
Transformers: 5.5.3
PyTorch: 2.11.0
PEFT: 0.18.1
BitsAndBytes: 0.49.2

Citation

@misc{azarkhalili2026qwen35_08b_sft_claude_reasoning,
    author = {Azarkhalili, Behrooz},
    title = {Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning: Fine-tuned Qwen3.5-0.8B for SFT Distillation},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/ermiaazarkhalili/Qwen3.5-0.8B-SFT-Claude-Opus-Reasoning}
}