Instructions to use jaweed123/Qwen3.5-0.8B-Python-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Transformers:

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

pipe = pipeline("text-generation", model="jaweed123/Qwen3.5-0.8B-Python-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaweed123/Qwen3.5-0.8B-Python-SFT")
model = AutoModelForCausalLM.from_pretrained("jaweed123/Qwen3.5-0.8B-Python-SFT", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Use Docker

docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaweed123/Qwen3.5-0.8B-Python-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

SGLang

How to use jaweed123/Qwen3.5-0.8B-Python-SFT 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 "jaweed123/Qwen3.5-0.8B-Python-SFT" \
    --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": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"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 "jaweed123/Qwen3.5-0.8B-Python-SFT" \
        --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": "jaweed123/Qwen3.5-0.8B-Python-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Ollama:
```
ollama run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
```
Unsloth Desktop

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Docker Model Runner:
```
docker model run hf.co/jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M
```

Lemonade

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-Python-SFT-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jaweed123/Qwen3.5-0.8B-Python-SFT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jaweed123/Qwen3.5-0.8B-Python-SFT:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

jaweed123 commited on Aug 7

Commit

deb2aa4

verified ·

1 Parent(s): 6ea2c96

Add model card

Browse files

Files changed (1) hide show

README.md +123 -0

README.md ADDED Viewed

	@@ -0,0 +1,123 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-0.8B-Base
+tags:
+  - qwen
+  - python
+  - code-generation
+  - qlora
+  - sft
+  - unsloth
+  - gguf
+datasets:
+  - code-search-net/code_search_net
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+# Qwen3.5-0.8B-Python-SFT
+**Python code generation model** — Qwen3.5-0.8B-Base fine-tuned with **QLoRA
+(Supervised Fine-Tuning)** on **CodeSearchNet (Python)**: docstring → function
+code pairs.
+## Model Details
+| Property | Value |
+|----------|-------|
+| Base model | [Qwen/Qwen3.5-0.8B-Base](https://huggingface.co/Qwen/Qwen3.5-0.8B-Base) |
+| Method | QLoRA (4-bit base + LoRA r=16, alpha=32) |
+| Trainable params | 6.4M / 759M (0.84%) |
+| Dataset | CodeSearchNet Python — 408K samples (13,590 repos) |
+| Task | Docstring → Python function code |
+| Sequence length | 2048 |
+| Precision | BF16 |
+| Hardware | NVIDIA RTX 4060 8GB |
+## Training Results
+| Metric | Value |
+|--------|-------|
+| Train loss | 0.330 |
+| Eval loss | 1.214 |
+| Steps | 25,524 (1 epoch) |
+| Runtime | ~25.7h |
+## Evaluation
+pass@1 (temperature 0.2), official test harness, both models in bf16.
+| Benchmark | Base | **Fine-tuned** | Improvement |
+|-----------|-----:|---------------:|------------:|
+| HumanEval | 1.2% | **17.7%** | 14.5x |
+| MBPP | 0.0% | 0.2% | 0 → 1 |
+Full report with example solutions: `reports/evaluation_report.md` in the training repo.
+## Training Details
+- **Method:** QLoRA — 4-bit quantized base + LoRA (r=16, alpha=32, dropout=0)
+- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Optimizer:** adamw_8bit (bitsandbytes), cosine schedule, 3% warmup
+- **Batch:** 2 per device × 8 grad accumulation (effective 16)
+- **Max sequence length:** 2048
+- **Hardware:** NVIDIA RTX 4060 8GB, ~25.7h
+- **Data:** CodeSearchNet Python filtered to ≤2048 tokens (408,377 train samples)
+## Usage
+### Transformers (LoRA adapter)
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "Qwen/Qwen3.5-0.8B-Base",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "jaweed123/Qwen3.5-0.8B-Python-SFT",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+```
+### GGUF (ollama / llama.cpp / vLLM)
+```bash
+# llama.cpp
+llama-cli -m qwen3.5-0.8b-python-sft-q4_k_m.gguf -p "Write a Python function that..."
+# Ollama
+ollama create qwen3.5-python -f Modelfile
+```
+```dockerfile
+# Modelfile
+FROM qwen3.5-0.8b-python-sft-q4_k_m.gguf
+TEMPLATE "{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+"
+```
+## Files
+| File | Description |
+|------|-------------|
+| `adapter_model.safetensors` | LoRA adapter (small, ~13MB) |
+| `model.safetensors` | Merged 16-bit model |
+| `qwen3.5-0.8b-python-sft-q4_k_m.gguf` | GGUF Q4_K_M (~0.5GB) |
+| `qwen3.5-0.8b-python-sft-q8_0.gguf` | GGUF Q8_0 (~0.9GB) |
+| `qwen3.5-0.8b-python-sft-f16.gguf` | GGUF F16 |
+## Limitations
+- Fine-tuned for Python function generation from docstrings
+- Trained on 2019-era open-source code
+- 0.8B scale — limited reasoning; best for straightforward code tasks