Instructions to use tufanakbas23/Pusula-danisman-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tufanakbas23/Pusula-danisman-ai 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 tufanakbas23/Pusula-danisman-ai:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tufanakbas23/Pusula-danisman-ai:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tufanakbas23/Pusula-danisman-ai:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tufanakbas23/Pusula-danisman-ai: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 tufanakbas23/Pusula-danisman-ai:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tufanakbas23/Pusula-danisman-ai: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 tufanakbas23/Pusula-danisman-ai:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tufanakbas23/Pusula-danisman-ai:Q4_K_M

Use Docker

docker model run hf.co/tufanakbas23/Pusula-danisman-ai:Q4_K_M

LM Studio
Jan

vLLM

How to use tufanakbas23/Pusula-danisman-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tufanakbas23/Pusula-danisman-ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tufanakbas23/Pusula-danisman-ai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tufanakbas23/Pusula-danisman-ai:Q4_K_M

Ollama
How to use tufanakbas23/Pusula-danisman-ai with Ollama:
```
ollama run hf.co/tufanakbas23/Pusula-danisman-ai:Q4_K_M
```
Unsloth Desktop

How to use tufanakbas23/Pusula-danisman-ai with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tufanakbas23/Pusula-danisman-ai:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tufanakbas23/Pusula-danisman-ai:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tufanakbas23/Pusula-danisman-ai with Docker Model Runner:
```
docker model run hf.co/tufanakbas23/Pusula-danisman-ai:Q4_K_M
```

Lemonade

How to use tufanakbas23/Pusula-danisman-ai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tufanakbas23/Pusula-danisman-ai:Q4_K_M

Run and chat with the model

lemonade run user.Pusula-danisman-ai-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tufanakbas23/Pusula-danisman-ai with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tufanakbas23/Pusula-danisman-ai: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 tufanakbas23/Pusula-danisman-ai:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tufanakbas23/Pusula-danisman-ai with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tufanakbas23/Pusula-danisman-ai: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 "tufanakbas23/Pusula-danisman-ai: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"

tufanakbas23

Rudblest commited on May 19

Commit

9680120

0 Parent(s):

Duplicate from Rudblest/projedanismanai

Browse files

Co-authored-by: Berkay parçal <Rudblest@users.noreply.huggingface.co>

Files changed (8) hide show

.gitattributes +37 -0
README.md +105 -0
config.json +30 -0
generation_config.json +6 -0
mistral-nemo-instruct-2407.Q4_K_M.gguf +3 -0
model.safetensors +3 -0
tokenizer.json +3 -0
tokenizer_config.json +13 -0

.gitattributes ADDED Viewed

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+mistral-nemo-instruct-2407.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

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+---
+language:
+- tr
+license: apache-2.0
+base_model: mistralai/Mistral-Nemo-Instruct-2407
+tags:
+- mistral
+- turkish
+- teknofest
+- tubitak
+- fine-tuned
+- qlora
+- unsloth
+pipeline_tag: text-generation
+---
+# ProjeDanışmanAi
+**TEKNOFEST ve TÜBİTAK yarışmacıları için Türkçe yapay zeka danışmanı.**
+Mistral-Nemo-Instruct-2407 (12B) modeli, TEKNOFEST ve TÜBİTAK yarışma süreçlerine özel Türkçe veri setiyle fine-tune edilmiştir.
+---
+## Model Detayları
+| Özellik | Değer |
+|---|---|
+| Temel Model | mistralai/Mistral-Nemo-Instruct-2407 |
+| Yöntem | QLoRA 4-bit + unsloth |
+| LoRA Rank | 64 (alpha=128, rsLoRA=True) |
+| Eğitim Verisi | 3043 Türkçe instruction-output çifti |
+| Epoch | 5 |
+| Max Seq Length | 3072 |
+| Train Loss | 0.3591 |
+---
+## Kullanım Alanları
+- TEKNOFEST KTR/PTR teknik rapor yazımı
+- TÜBİTAK başvuru hazırlığı
+- Proje fikri netleştirme
+- Risk analizi ve uygulanabilirlik değerlendirmesi
+- Başlık ve özet üretimi
+- Jüri ve sunum hazırlığı
+---
+## Veri Seti
+3043 Türkçe örnekten oluşan özel veri seti:
+| Kategori | Örnek Sayısı |
+|---|---|
+| rapor_yazimi | 761 |
+| sifirdan_proje | 730 |
+| genel_ozet | 593 |
+| strateji | 505 |
+| hata_duzeltme | 324 |
+| red (alan dışı red) | 130 |
+---
+## Kullanım
+### GGUF (Ollama ile)
+```bash
+ollama create projedanismanai -f Modelfile
+ollama run projedanismanai
+```
+### Python (unsloth ile)
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name     = "Rudblest/projedanismanai",
+    max_seq_length = 3072,
+    dtype          = None,
+    load_in_4bit   = True,
+)
+FastLanguageModel.for_inference(model)
+prompt = "<s>[INST] TEKNOFEST KTR raporunda risk analizi nasıl yazılır? [/INST] "
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+---
+## Sınırlamalar
+- Yalnızca Türkçe cevap verir
+- Alan dışı sorular (yemek, borsa, sağlık vb.) reddedilir
+- TEKNOFEST/TÜBİTAK dışı mühendislik konularında performans düşebilir
+---
+## Lisans
+Apache 2.0 — Mistral-Nemo temel modeli lisansına uygun.

config.json ADDED Viewed

	@@ -0,0 +1,30 @@

+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "vocab_size": 131072
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "5.5.0"
+}

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+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>"
+}