Instructions to use nlpguy/ColorShadow-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpguy/ColorShadow-7B with Transformers:

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

pipe = pipeline("text-generation", model="nlpguy/ColorShadow-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpguy/ColorShadow-7B")
model = AutoModelForCausalLM.from_pretrained("nlpguy/ColorShadow-7B")
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

vLLM

How to use nlpguy/ColorShadow-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpguy/ColorShadow-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpguy/ColorShadow-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nlpguy/ColorShadow-7B

SGLang

How to use nlpguy/ColorShadow-7B 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 "nlpguy/ColorShadow-7B" \
    --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": "nlpguy/ColorShadow-7B",
		"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 "nlpguy/ColorShadow-7B" \
        --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": "nlpguy/ColorShadow-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nlpguy/ColorShadow-7B with Docker Model Runner:
```
docker model run hf.co/nlpguy/ColorShadow-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ColorShadow-7B

This is a Gradient-SLERP merge between diffnamehard/Mistral-CatMacaroni-slerp-7B and cookinai/Valkyrie-V1 performed using mergekit.

Here is the config file used:

  slices:
    - sources:
        - model: diffnamehard/Mistral-CatMacaroni-slerp-7B
          layer_range: [0, 32]
        - model: cookinai/Valkyrie-V1
          layer_range: [0, 32]
  merge_method: slerp
  base_model: diffnamehard/Mistral-CatMacaroni-slerp-7B
  parameters:
    t:
      - filter: self_attn
        value: [0, 0.5, 0.3, 0.7, 1]
      - filter: mlp
        value: [1, 0.5, 0.7, 0.3, 0]
      - value: 0.5 # fallback for rest of tensors
  dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.34
AI2 Reasoning Challenge (25-Shot)	67.83
HellaSwag (10-Shot)	85.15
MMLU (5-Shot)	61.69
TruthfulQA (0-shot)	59.56
Winogrande (5-shot)	80.58
GSM8k (5-shot)	55.19

Downloads last month: 221

Safetensors

Model size

7B params

Tensor type

F16

Model tree for nlpguy/ColorShadow-7B

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

67.830
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.150
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

61.690
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

59.560
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.580
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

55.190