Instructions to use ockerman0/EVA-Tissint-v1.1-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ockerman0/EVA-Tissint-v1.1-14B with Transformers:

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

pipe = pipeline("text-generation", model="ockerman0/EVA-Tissint-v1.1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ockerman0/EVA-Tissint-v1.1-14B")
model = AutoModelForCausalLM.from_pretrained("ockerman0/EVA-Tissint-v1.1-14B")
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 ockerman0/EVA-Tissint-v1.1-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ockerman0/EVA-Tissint-v1.1-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ockerman0/EVA-Tissint-v1.1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ockerman0/EVA-Tissint-v1.1-14B

SGLang

How to use ockerman0/EVA-Tissint-v1.1-14B 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 "ockerman0/EVA-Tissint-v1.1-14B" \
    --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": "ockerman0/EVA-Tissint-v1.1-14B",
		"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 "ockerman0/EVA-Tissint-v1.1-14B" \
        --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": "ockerman0/EVA-Tissint-v1.1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ockerman0/EVA-Tissint-v1.1-14B with Docker Model Runner:
```
docker model run hf.co/ockerman0/EVA-Tissint-v1.1-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EVA-Tissint-v1.1-14B

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

New merge of EVA v0.2, now using Tissint v1.1. Hopefully the new version of Tissint and slight change of merge settings make an improvement over the first iteration.

I recommend the samplers provided on Tissint's model card.

If you'd like to use XTC, I recommend a threshold of 0.2. Lower thresholds seem to adversely affect the coherency.

Quantisations

Static: https://huggingface.co/mradermacher/EVA-Tissint-v1.1-14B-GGUF

Imatrix: https://huggingface.co/mradermacher/EVA-Tissint-v1.1-14B-i1-GGUF

Merge Method

This model was merged using the della_linear merge method using EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2 as a base.

Models Merged

The following models were included in the merge:

Ttimofeyka/Tissint-14B-v1.1-128k-RP

Configuration

The following YAML configuration was used to produce this model:

models:
    - model: Ttimofeyka/Tissint-14B-v1.1-128k-RP
      parameters:
        density: 0.45
        weight: 0.3
    - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
      parameters:
        density: 0.55
        weight: 0.7

merge_method: della_linear
base_model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
parameters:
    epsilon: 0.05
    lambda: 1
dtype: bfloat16

Downloads last month: -

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for ockerman0/EVA-Tissint-v1.1-14B

Quantizations

2 models