Instructions to use ImposterOnline/Pyrex-8B-Instruct-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ImposterOnline/Pyrex-8B-Instruct-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="ImposterOnline/Pyrex-8B-Instruct-Uncensored")
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("ImposterOnline/Pyrex-8B-Instruct-Uncensored")
model = AutoModelForCausalLM.from_pretrained("ImposterOnline/Pyrex-8B-Instruct-Uncensored", 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

vLLM

How to use ImposterOnline/Pyrex-8B-Instruct-Uncensored with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ImposterOnline/Pyrex-8B-Instruct-Uncensored

SGLang

How to use ImposterOnline/Pyrex-8B-Instruct-Uncensored 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 "ImposterOnline/Pyrex-8B-Instruct-Uncensored" \
    --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": "ImposterOnline/Pyrex-8B-Instruct-Uncensored",
		"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 "ImposterOnline/Pyrex-8B-Instruct-Uncensored" \
        --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": "ImposterOnline/Pyrex-8B-Instruct-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ImposterOnline/Pyrex-8B-Instruct-Uncensored with Docker Model Runner:
```
docker model run hf.co/ImposterOnline/Pyrex-8B-Instruct-Uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Pyrex-8B-Instruct-Uncensored

Pyrex Benchmark

Pyrex-8B-Instruct-Uncensored is a state-of-the-art, uncensored coding model designed for high-level software engineering, multi-turn debugging, and agentic function calling.

1. Model Overview

Property	Value
Architecture	Pyrex 8B Transformer (RoPE, SwiGLU, RMSNorm, GQA)
Parameters	7.6 Billion (8B Class)
Context Length	Native 32,768 tokens (trained at 4,096 sequence length)
Primary Domain	Software engineering, algorithm design, system architecture, function calling
Formats	Safetensors (bf16), GGUF (`Q4_K_M`, `Q5_K_M`, `Q8_0`, `f16`)
License	MIT

2. Benchmark Dashboard

All benchmarks evaluated with strict greedy decoding (temp=0.0) on unseen tasks with sandbox unit test execution.

Coding Benchmarks

Evaluation Metric	Score	Split / Details	Hardware
HumanEval (pass@1)	87.20% (143 / 164)	Test split, greedy	Kaggle T4 GPU
HumanEval (pass@1) - Run 2	85.98% (141 / 164)	Test split, greedy	Kaggle T4 GPU
MBPP (pass@1)	6.23% (16 / 257)	Test split, greedy	Kaggle T4 GPU
SWE Debugging (pass@1)	100% (3 / 3)	Custom bug-fixing suite	Kaggle T4 GPU
Pyrex v1 Pass@1	52.40% (86 / 164)	Baseline fine-tune	-
Pyrex Untuned Pass@1	29.30% (48 / 164)	Untuned baseline weights	-

Benchmark Progression

Pyrex Untuned Baseline:            29.3% ┃▓▓▓▓▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
Pyrex Baseline Fine-tune:          52.4% ┃▓▓▓▓▓▓▓▓▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
Pyrex-8B (Final, Run 1):           87.2% ┃▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
Pyrex-8B (Final, Run 2 - Verified): 86.0% ┃▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░

3. Training History

Parameter	Value
Dataset Mixture	95k cleaned examples (`OpenCoder-SFT-Stage1`, `CodeAlpaca-20k`, `Hermes-Function-Calling-v1`)
Optimization	QLoRA (NF4 4-bit, r=48, α=96), AdamW, Cosine schedule (lr=2e-4)
Hardware	Kaggle T4 / HF GPU Compute Cluster
Loss Masking	Completion-only (assistant turn loss masking)
Sequence Length	4,096 tokens
Epochs	1

Training Experiment Log

Run ID	Date	Dataset	Epochs	LR	Pass@1	Status
v1-baseline	2026-10-01	8k examples	1	2e-4	42.1%	Completed
v1-final	2026-10-03	95k examples	1	2e-4	52.4%	Completed
v2-run1	2026-10-10	95k examples	1	2e-4	87.20%	Completed
v2-run2	2026-10-10	95k examples	1	2e-4	85.98%	Completed

4. Usage & Inference

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ImposterOnline/Pyrex-8B-Instruct-Uncensored"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

chat = [{"role": "user", "content": "Write a python function to find the longest palindromic substring."}]
inputs = tokenizer(tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True), return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Local GGUF (Ollama / llama.cpp)

Available in ImposterOnline/Pyrex-8B-Instruct-Uncensored-GGUF:

# Ollama
ollama create pyrex-8b -f Modelfile
ollama run pyrex-8b "Write a Python script to monitor system memory."

# llama.cpp
./llama-cli -m pyrex-q4_k_m.gguf -p "
</think>

" -n 512 -c 8192

5. Model Limitations & Safety

Uncensored: This model has no safety filters. It will fulfill technical requests directly without refusal or ethical lecturing.
Verification: Outputs should always be tested in sandboxed environments for production critical paths.
Context Length: Trained at 4K, supports up to 32K natively. Long-context performance unverified beyond training length.

Provenance

Architecture: Pyrex 8B Transformer (original design)
Training Data: OpenCoder-LLM/opencoder-sft-stage1, sahil2801/CodeAlpaca-20k, NousResearch/hermes-function-calling-v1
Pipeline: Config-driven QLoRA fine-tuning (see github.com/Arhan-w/pyrut)
Evaluation: Real GPU execution on Kaggle T4, greedy pass@1, full test suite verification

Pyrex is an independent project dedicated to unconstrained, high-performance software engineering.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ImposterOnline/Pyrex-8B-Instruct-Uncensored

Quantizations

4 models

Spaces using ImposterOnline/Pyrex-8B-Instruct-Uncensored 3

Evaluation results

pass@1 (greedy) on HumanEval
self-reported

87.200