Instructions to use Jackrong/GPT-5-Distill-llama3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/GPT-5-Distill-llama3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Jackrong/GPT-5-Distill-llama3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jackrong/GPT-5-Distill-llama3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Jackrong/GPT-5-Distill-llama3.1-8B-Instruct", 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 Jackrong/GPT-5-Distill-llama3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/GPT-5-Distill-llama3.1-8B-Instruct

SGLang

How to use Jackrong/GPT-5-Distill-llama3.1-8B-Instruct 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 "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct" \
    --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": "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct",
		"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 "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct" \
        --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": "Jackrong/GPT-5-Distill-llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Jackrong/GPT-5-Distill-llama3.1-8B-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/GPT-5-Distill-llama3.1-8B-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/GPT-5-Distill-llama3.1-8B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/GPT-5-Distill-llama3.1-8B-Instruct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/GPT-5-Distill-llama3.1-8B-Instruct",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jackrong/GPT-5-Distill-llama3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/Jackrong/GPT-5-Distill-llama3.1-8B-Instruct
```

GPT-5-Distill-llama3.1-8B-Instruct

Model Summary

GPT-5-Distill-llama3.1-8B-Instruct is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct, designed to distill the capabilities of high-performance models (labeled as GPT-5 in source datasets) into a more efficient 8B parameter footprint.

This model was trained using Unsloth on a curated mix of approximately 164,000 high-quality instruction-response pairs, focusing on complex reasoning and "normal" flaw-level responses.

Base Model: meta-llama/Llama-3.1-8B-Instruct
Architecture: Llama 3.1 (8B parameters)
Language: English (Primary)
Context Window: 32,768 tokens
Fine-tuning Framework: Unsloth (QLoRA)

✨ Key Advantages of GPT-5 Distillation

This model represents a shift towards "Super-Knowledge Distillation", where a smaller, efficient student model learns from a significantly more capable teacher.

🚀 Frontier-Level Reasoning: By training on dataset samples attributed to GPT-5, the model acquires complex reasoning patterns, nuance, and problem-solving strategies that are typically absent in standard datasets or smaller models.
⚡ Efficient Intelligence: Users can experience high-fidelity, coherent, and detailed responses on consumer hardware (e.g., single GPUs) without the latency, privacy concerns, or cost of querying giant proprietary APIs.
💎 High-Purity Signal: The strict filtering for flaw == "normal" ensures the model is fine-tuned only on the highest confidence, error-free responses. This minimizes "hallucination inheritance" and aligns the model with safe, helpful behaviors.
🎯 Enhanced Nuance & Tone: Unlike standard finetunes that often sound robotic, this model mimics the more natural, conversational, and adaptive tone found in next-generation frontier models.

📚 Training Data

The model was trained on a high-quality blend of two datasets, totaling 163,896 samples:

Chat-GPT-5-Chat-Response (160k samples)
- Filtered specifically for normal entries to ensure high-quality, safe, and coherent responses.
- This dataset serves as the primary distillation source, aiming to mimic the response patterns of advanced large language models.
ShareGPT-Qwen3-235B-A22B-Instuct-2507 (3.9k samples)
- "This dataset consists of approximately 3.9k examples, with an average of about 5 rounds of dialogue per scenario, designed to enhance the model’s instruction-following ability and task-completion efficiency.

All data was formatted using the standard Llama-3 Chat Template.

⚙️ Training Details

Hardware: NVIDIA H100
Sequence Length: 32,768 tokens (Long Context Support)
Batch Size: 4 per device (Effective Batch Size: 32 via Gradient Accumulation)
Learning Rate: 2e-5
Scheduler: Linear
Optimizer: AdamW 8-bit
LoRA Rank (r): 32
LoRA Alpha: 32
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

🛡️ License & Limitations

License: This model is subject to the Llama 3.1 Community License.
Limitations: While this model is distilled from high-capability sources, it is still an 8B parameter model. It may hallucinate facts or struggle with extremely complex reasoning tasks compared to the original teacher models. The "GPT-5" naming refers to the source dataset labels and does not imply access to unreleased OpenAI weights.