Instructions to use joelauge/RecourseLLM_Q8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joelauge/RecourseLLM_Q8b with Transformers:

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

pipe = pipeline("text-generation", model="joelauge/RecourseLLM_Q8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joelauge/RecourseLLM_Q8b")
model = AutoModelForCausalLM.from_pretrained("joelauge/RecourseLLM_Q8b")
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 joelauge/RecourseLLM_Q8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joelauge/RecourseLLM_Q8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joelauge/RecourseLLM_Q8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joelauge/RecourseLLM_Q8b

SGLang

How to use joelauge/RecourseLLM_Q8b 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 "joelauge/RecourseLLM_Q8b" \
    --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": "joelauge/RecourseLLM_Q8b",
		"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 "joelauge/RecourseLLM_Q8b" \
        --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": "joelauge/RecourseLLM_Q8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use joelauge/RecourseLLM_Q8b with Docker Model Runner:
```
docker model run hf.co/joelauge/RecourseLLM_Q8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RecourseLLM_Q8b

This model is a derivative of Qwen/Qwen3-8B, licensed under Apache 2.0.
Original model by the Qwen Team. See license and attribution below.

About RecourseLLM

RecourseLLM focuses on Token Optimization Analysis — helping organizations understand, measure, and optimize token usage in large language model deployments.

Our work centers on:

Token efficiency analysis
Cost-per-inference optimization
Reasoning verbosity benchmarking
Prompt-to-token compression strategies
Agent loop token amplification control
Enterprise LLM cost modeling

This fork of Qwen3-8B has been adapted to support advanced token instrumentation and optimization research workflows.

What Has Been Modified

This derivative version of Qwen3-8B has been adapted for:

Token usage instrumentation
Output-length profiling
Think-block efficiency benchmarking
Token density and redundancy analysis
Structured token logging compatibility
Prompt compression experimentation
Cost-performance modeling

No architectural claims are made beyond those of the original Qwen3-8B base model. Core weights originate from the Apache-licensed base model.

Purpose of This Fork

RecourseLLM_Q8b is designed for:

Token efficiency benchmarking
Enterprise cost optimization modeling
Prompt engineering research
Agent workflow token diagnostics
Reasoning verbosity analysis
SaaS inference cost reduction experiments

The primary objective is token performance analysis, not altering the fundamental capabilities of the base model.

Base Model Attribution

This model is based on:

Qwen3-8B

License: Apache 2.0
Authors: Qwen Team
Original Repository: https://huggingface.co/Qwen/Qwen3-8B

Original license:
https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE

All original copyrights and license terms remain in effect.

Model Overview (Inherited from Qwen3-8B)

Type: Causal Language Model
Parameters: 8 Billion
Architecture: Dense Transformer
Training Stage: Pretraining & Post-training
Context Length: 32,768 tokens
Supports thinking and non-thinking modes

For full architecture details and benchmark results, please refer to:

Blog: https://qwenlm.github.io/blog/qwen3/
GitHub: https://github.com/QwenLM/Qwen3
Documentation: https://qwen.readthedocs.io/en/latest/

Token Optimization Analysis Capabilities

This fork enables structured token analysis across high-capacity reasoning tasks.

1️⃣ Think vs Non-Think Efficiency Benchmarking

Measure:

Reasoning token overhead
Think-block token ratio
Final-answer compression ratio
Accuracy-per-token delta
Marginal cost per reasoning step

2️⃣ Token Density Profiling

Analyze:

Tokens per semantic unit
Redundancy rates
Repetition patterns
Token-per-concept ratio
Verbosity distribution curves

3️⃣ Enterprise Cost Modeling

Supports:

Per-request token cost estimation
Token budget simulation
Throughput-to-token efficiency modeling
Multi-agent loop token amplification tracking
Scaled inference cost projections

4️⃣ Prompt Compression Experiments

Evaluate:

Minimal prompt vs verbose prompt tradeoffs
System prompt token footprint
Multi-turn token growth curves
Prompt entropy vs output quality
Instruction density optimization

Intended Use

RecourseLLM_Q8b is intended for:

Enterprise LLM cost analysis
Token efficiency research
Prompt engineering optimization
Agent orchestration benchmarking
Controlled inference environments
Token instrumentation experiments

It is not intended as a production conversational deployment without additional tuning and evaluation.

Licensing

This repository includes Apache 2.0 licensed components from the original Qwen3-8B model.

Your use of this model must comply with the Apache 2.0 License.

Modifications introduced by RecourseLLM are distributed in compliance with Apache 2.0. Additional usage terms may apply depending on deployment context.

Citation

If you find the original Qwen3 work helpful, please cite:

@misc{qwen3technicalreport,
  title={Qwen3 Technical Report},
  author={Qwen Team},
  year={2025},
  eprint={2505.09388},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2505.09388}
}