Instructions to use nazimali/Mistral-Nemo-Kurdish-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="nazimali/Mistral-Nemo-Kurdish-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nazimali/Mistral-Nemo-Kurdish-Instruct")
model = AutoModelForCausalLM.from_pretrained("nazimali/Mistral-Nemo-Kurdish-Instruct", 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]:]))

llama-cpp-python

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nazimali/Mistral-Nemo-Kurdish-Instruct",
	filename="BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Use Docker

docker model run hf.co/nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

SGLang

How to use nazimali/Mistral-Nemo-Kurdish-Instruct 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 "nazimali/Mistral-Nemo-Kurdish-Instruct" \
    --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": "nazimali/Mistral-Nemo-Kurdish-Instruct",
		"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 "nazimali/Mistral-Nemo-Kurdish-Instruct" \
        --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": "nazimali/Mistral-Nemo-Kurdish-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Ollama:
```
ollama run hf.co/nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
```

Unsloth Studio

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nazimali/Mistral-Nemo-Kurdish-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nazimali/Mistral-Nemo-Kurdish-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nazimali/Mistral-Nemo-Kurdish-Instruct to start chatting

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Docker Model Runner:
```
docker model run hf.co/nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M
```

Lemonade

How to use nazimali/Mistral-Nemo-Kurdish-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nazimali/Mistral-Nemo-Kurdish-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Nemo-Kurdish-Instruct-Q4_K_M

List all available models

lemonade list

leaderboard-pr-bot commited on Oct 16, 2024

Commit

bcd96a5

verified ·

1 Parent(s): 5121405

Adding Evaluation Results

Browse files

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Files changed (1) hide show

README.md +113 -5

README.md CHANGED Viewed

@@ -1,20 +1,115 @@
 ---
-base_model:
-- nazimali/Mistral-Nemo-Kurdish
-base_model_relation: finetune
 language:
 - ku
 - en
 license: apache-2.0
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - mistral
 - gguf
 datasets:
 - saillab/alpaca-kurdish_kurmanji-cleaned
-library_name: transformers
 ---
 <div dir="auto" align="right">
@@ -180,4 +275,17 @@ finetune_prompt = """Li jêr rêwerzek heye ku peywirek rave dike, bi têketinek
 ### Bersiv:
 {}
 """
-```

 ---
 language:
 - ku
 - en
 license: apache-2.0
+library_name: transformers
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - mistral
 - gguf
+base_model:
+- nazimali/Mistral-Nemo-Kurdish
 datasets:
 - saillab/alpaca-kurdish_kurmanji-cleaned
+base_model_relation: finetune
+model-index:
+- name: Mistral-Nemo-Kurdish-Instruct
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 48.6
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 26.02
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 0.3
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 4.59
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 8.84
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 23.19
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nazimali/Mistral-Nemo-Kurdish-Instruct
+      name: Open LLM Leaderboard
 ---
 <div dir="auto" align="right">
 ### Bersiv:
 {}
 """
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nazimali__Mistral-Nemo-Kurdish-Instruct)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |18.59|
+|IFEval (0-Shot)    |48.60|
+|BBH (3-Shot)       |26.02|
+|MATH Lvl 5 (4-Shot)| 0.30|
+|GPQA (0-shot)      | 4.59|
+|MuSR (0-shot)      | 8.84|
+|MMLU-PRO (5-shot)  |23.19|