Instructions to use Mihaiii/Covasna-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mihaiii/Covasna-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Mihaiii/Covasna-0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mihaiii/Covasna-0.1")
model = AutoModelForCausalLM.from_pretrained("Mihaiii/Covasna-0.1")
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 Mihaiii/Covasna-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mihaiii/Covasna-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mihaiii/Covasna-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mihaiii/Covasna-0.1

SGLang

How to use Mihaiii/Covasna-0.1 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 "Mihaiii/Covasna-0.1" \
    --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": "Mihaiii/Covasna-0.1",
		"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 "Mihaiii/Covasna-0.1" \
        --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": "Mihaiii/Covasna-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mihaiii/Covasna-0.1 with Docker Model Runner:
```
docker model run hf.co/Mihaiii/Covasna-0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a BF16 and pruned version of migtissera/Tess-70B-v1.6 .

migtissera/Tess-70B-v1.6 has 69 billion params and Covasna-0.1 has 41.6 billion (~60.3% param size)

Steps to replicate:

Use laserQlora.ipynb from cognitivecomputations/laserRMT to determine which layers should be eliminated.

Adapt the script for migtissera/Tess-70B-v1.6 by replacing model_name = "mistralai/Mistral-7B-v0.1" with model_name = "migtissera/Tess-70B-v1.6" and layer_numbers = list(range(31, -1, -1)) with layer_numbers = list(range(79, -1, -1)), 79 being the last recurrent layer index Tess-70B-v1.6 has.

Then look for the layer indexes where self_attn.v_proj snr is Infinity and eliminate those layers using mergekit.

Here is the mergekit config:

slices:
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [0, 7]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [8, 9]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [12, 29]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [31, 32]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [33, 45]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [50, 52]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [60, 61]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [67, 68]
  - sources:
    - model: "migtissera/Tess-70B-v1.6"
      layer_range: [74, 80]
merge_method: passthrough
dtype: bfloat16

GGUF: Covasna-0.1-GGUF

Downloads last month: 4

Safetensors

Model size

42B params

Tensor type

BF16

Model tree for Mihaiii/Covasna-0.1

Base model

migtissera/Tess-70B-v1.6

Finetuned

(1)

this model

Quantizations

2 models