Instructions to use mohammedqarnuon/Qarnon-Ultra-V3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohammedqarnuon/Qarnon-Ultra-V3.5 with Transformers:

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

pipe = pipeline("text-generation", model="mohammedqarnuon/Qarnon-Ultra-V3.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mohammedqarnuon/Qarnon-Ultra-V3.5")
model = AutoModelForCausalLM.from_pretrained("mohammedqarnuon/Qarnon-Ultra-V3.5", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mohammedqarnuon/Qarnon-Ultra-V3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohammedqarnuon/Qarnon-Ultra-V3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammedqarnuon/Qarnon-Ultra-V3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohammedqarnuon/Qarnon-Ultra-V3.5

SGLang

How to use mohammedqarnuon/Qarnon-Ultra-V3.5 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 "mohammedqarnuon/Qarnon-Ultra-V3.5" \
    --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": "mohammedqarnuon/Qarnon-Ultra-V3.5",
		"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 "mohammedqarnuon/Qarnon-Ultra-V3.5" \
        --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": "mohammedqarnuon/Qarnon-Ultra-V3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mohammedqarnuon/Qarnon-Ultra-V3.5 with Docker Model Runner:
```
docker model run hf.co/mohammedqarnuon/Qarnon-Ultra-V3.5
```

mohammedqarnuon commited on Jun 27

Commit

72471f1

verified ·

1 Parent(s): d20043d

Add Qarnon-Ultra-V3.5 Moroccan Assistant

Browse files

Files changed (3) hide show

config.json +67 -32
generation_config.json +11 -3
model.safetensors +2 -2

config.json CHANGED Viewed

@@ -1,41 +1,76 @@
 {
-  "activation_function": "gelu_new",
-  "add_cross_attention": false,
   "architectures": [
-    "GPT2LMHeadModel"
   ],
-  "attn_pdrop": 0.1,
-  "bos_token_id": 50256,
-  "dtype": "float32",
-  "embd_pdrop": 0.1,
-  "eos_token_id": 50256,
   "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "model_type": "gpt2",
-  "n_ctx": 1024,
-  "n_embd": 768,
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-  "n_positions": 1024,
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-  "scale_attn_by_inverse_layer_idx": false,
-  "scale_attn_weights": true,
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-  "summary_proj_to_labels": true,
-  "summary_type": "cls_index",
-  "summary_use_proj": true,
-  "task_specific_params": {
-    "text-generation": {
-      "do_sample": true,
-      "max_length": 50
-    }
   },
   "tie_word_embeddings": true,
   "transformers_version": "5.12.0",
-  "use_cache": false,
-  "vocab_size": 50257
 }

 {
   "architectures": [
+    "Qwen2ForCausalLM"
   ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
   "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention"
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
   "pad_token_id": null,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "float16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
   },
+  "sliding_window": null,
   "tie_word_embeddings": true,
   "transformers_version": "5.12.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
 }

generation_config.json CHANGED Viewed

@@ -1,6 +1,14 @@
 {
-  "_from_model_config": true,
-  "bos_token_id": 50256,
-  "eos_token_id": 50256,
   "transformers_version": "5.12.0"
 }

 {
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
   "transformers_version": "5.12.0"
 }

model.safetensors CHANGED Viewed

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