Instructions to use grimjim/cuckoo-starling-32k-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/cuckoo-starling-32k-7B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/cuckoo-starling-32k-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/cuckoo-starling-32k-7B")
model = AutoModelForCausalLM.from_pretrained("grimjim/cuckoo-starling-32k-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/cuckoo-starling-32k-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/cuckoo-starling-32k-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": "grimjim/cuckoo-starling-32k-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/cuckoo-starling-32k-7B

SGLang

How to use grimjim/cuckoo-starling-32k-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 "grimjim/cuckoo-starling-32k-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": "grimjim/cuckoo-starling-32k-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 "grimjim/cuckoo-starling-32k-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": "grimjim/cuckoo-starling-32k-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use grimjim/cuckoo-starling-32k-7B with Docker Model Runner:
```
docker model run hf.co/grimjim/cuckoo-starling-32k-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

cuckoo-starling-32k-7B

For this merged model, rope theta was in config.json was manually adjusted down to 100K, a value less than 1M as initially released by Mistral for v0.2, but higher than the 10K that accompanied practical 8K context for v0.1. We idly conjecture that 1M rope theta might improve performance for needle-in-a-haystack queries; however, during informal testing, narrative coherence seemed to occasionally suffer under 1M rope theta. Furthermore, the results reported in the arXiv paper Scaling Laws of RoPE-based Extrapolation suggest that 1M rope theta may be overkill for a 32K token context window.

Lightly tested with temperature 0.9-1.0 and minP 0.02, using ChatML prompts. The model natively supports Alpaca prompts.

This is a merge of pre-trained language models created using mergekit.

Full weights: grimjim/cuckoo-starling-32k-7B
GGUFs: grimjim/cuckoo-starling-32k-7B-GGUF

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: grimjim/Mistral-Starling-merge-trial1-7B
        layer_range: [0, 32]
      - model: grimjim/kukulemon-7B
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: grimjim/Mistral-Starling-merge-trial1-7B
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: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.93
AI2 Reasoning Challenge (25-Shot)	66.81
HellaSwag (10-Shot)	85.97
MMLU (5-Shot)	64.88
TruthfulQA (0-shot)	59.03
Winogrande (5-shot)	80.11
GSM8k (5-shot)	62.77