Instructions to use utkmst/chimera-beta-test2-lora-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use utkmst/chimera-beta-test2-lora-merged with Transformers:

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

pipe = pipeline("text-generation", model="utkmst/chimera-beta-test2-lora-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("utkmst/chimera-beta-test2-lora-merged")
model = AutoModelForCausalLM.from_pretrained("utkmst/chimera-beta-test2-lora-merged", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use utkmst/chimera-beta-test2-lora-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "utkmst/chimera-beta-test2-lora-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utkmst/chimera-beta-test2-lora-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/utkmst/chimera-beta-test2-lora-merged

SGLang

How to use utkmst/chimera-beta-test2-lora-merged 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 "utkmst/chimera-beta-test2-lora-merged" \
    --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": "utkmst/chimera-beta-test2-lora-merged",
		"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 "utkmst/chimera-beta-test2-lora-merged" \
        --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": "utkmst/chimera-beta-test2-lora-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use utkmst/chimera-beta-test2-lora-merged with Docker Model Runner:
```
docker model run hf.co/utkmst/chimera-beta-test2-lora-merged
```

utkmst/chimera-beta-test2-lora-merged

Model Description

This model is a fine-tuned version of Meta's Llama-3.1-8B-Instruct model, created through LoRA fine-tuning on multiple instruction datasets, followed by merging the adapter weights with the base model.

Architecture

Base Model: meta-llama/Llama-3.1-8B-Instruct
Size: 8.03B parameters
Type: Decoder-only transformer
Format: SafeTensors (full precision)

Training Details

Training Method: LoRA fine-tuning followed by adapter merging
LoRA Configuration:
- Rank: 8
- Alpha: 16
- Trainable modules: Attention layers and feed-forward networks
Training Hyperparameters:
- Learning rate: 2e-4
- Batch size: 2
- Training epochs: 1
- Optimizer: AdamW with constant scheduler

Intended Use

This model is designed for:

General purpose assistant capabilities
Question answering and knowledge retrieval
Creative content generation
Instructional guidance

Limitations

Base model limitations including potential hallucinations and factual inaccuracies
Limited context window compared to larger models
Knowledge cutoff from the base Llama-3.1 model
May exhibit biases present in training data
Performance on specialized tasks may vary

Usage with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("utkmst/chimera-beta-test2-lora-merged")
tokenizer = AutoTokenizer.from_pretrained("utkmst/chimera-beta-test2-lora-merged")

License

This model inherits the license from Meta's Llama 3.1.

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F16

Model tree for utkmst/chimera-beta-test2-lora-merged

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2890)

this model

Datasets used to train utkmst/chimera-beta-test2-lora-merged

Evaluation results

acc_norm on Overall Leaderboard
self-reported

0.444
acc on Overall Leaderboard
self-reported

0.299
exact_match on Overall Leaderboard
self-reported

0.095
acc_norm on BBH (Big Bench Hard)
self-reported

0.477
acc_norm on GPQA (Google-Patched Question Answering)
self-reported

0.304
exact_match on Math
self-reported

0.095
acc on MMLU-Pro
self-reported

0.299
acc_norm on MUSR (Multi-Step Reasoning)
self-reported

0.411