Instructions to use PS4Research/Y4KmA2F7i00O2Roi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PS4Research/Y4KmA2F7i00O2Roi with Transformers:

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

pipe = pipeline("text-generation", model="PS4Research/Y4KmA2F7i00O2Roi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PS4Research/Y4KmA2F7i00O2Roi")
model = AutoModelForCausalLM.from_pretrained("PS4Research/Y4KmA2F7i00O2Roi", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PS4Research/Y4KmA2F7i00O2Roi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PS4Research/Y4KmA2F7i00O2Roi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PS4Research/Y4KmA2F7i00O2Roi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PS4Research/Y4KmA2F7i00O2Roi

SGLang

How to use PS4Research/Y4KmA2F7i00O2Roi 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 "PS4Research/Y4KmA2F7i00O2Roi" \
    --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": "PS4Research/Y4KmA2F7i00O2Roi",
		"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 "PS4Research/Y4KmA2F7i00O2Roi" \
        --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": "PS4Research/Y4KmA2F7i00O2Roi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use PS4Research/Y4KmA2F7i00O2Roi with Docker Model Runner:
```
docker model run hf.co/PS4Research/Y4KmA2F7i00O2Roi
```

PS4Research commited on 29 days ago

Commit

95de35c

verified ·

1 Parent(s): f4c0e4a

(Trained with Unsloth)

Browse files

Files changed (5) hide show

chat_template.jinja +1 -0
config.json +35 -0
generation_config.json +13 -0
tokenizer.json +0 -0
tokenizer_config.json +792 -0

chat_template.jinja ADDED Viewed

	@@ -0,0 +1 @@

+ {% if messages[0]['role'] != 'system' %}{{'<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>'}}{% endif %}{% for message in messages %}{% if messages[0]['role'] == 'system' %}{{'<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>'}}{% elif message['role'] == 'user' %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}

config.json ADDED Viewed

	@@ -0,0 +1,35 @@

+{
+    "architectures": [
+        "Phi3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 100257,
+    "torch_dtype": "bfloat16",
+    "embd_pdrop": 0.0,
+    "eos_token_id": 100265,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 17920,
+    "max_position_embeddings": 32768,
+    "model_type": "phi3",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 10,
+    "original_max_position_embeddings": 32768,
+    "pad_token_id": 100349,
+    "partial_rotary_factor": 1.0,
+    "resid_pdrop": 0.0,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "partial_rotary_factor": 1.0,
+        "rope_theta": 500000,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "use_cache": true,
+    "vocab_size": 100352
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,13 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 100257,
+  "do_sample": true,
+  "eos_token_id": [
+    100265
+  ],
+  "max_length": 32768,
+  "pad_token_id": 100349,
+  "temperature": 0.8,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

tokenizer.json ADDED Viewed

The diff for this file is too large to render. See raw diff

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,792 @@

+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|dummy_85|>",
+  "padding_side": "right",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "ï¿½",
+  "added_tokens_decoder": {
+    "5809": {
+      "content": "ï¿½",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "100256": {
+      "content": "<|dummy_0|>",
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+    },
+    "100257": {
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+      "single_word": false,
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+    },
+    "100260": {
+      "content": "<|fim_suffix|>",
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+    },
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+      "single_word": false,
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+    },
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+    },
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+    },
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+    },
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+      "special": true
+    },
+    "100297": {
+      "content": "<|dummy_33|>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    },
+    "100298": {
+      "content": "<|dummy_34|>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    },
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+      "content": "<|dummy_35|>",
+      "single_word": false,
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+      "special": true
+    },
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+      "content": "<|dummy_36|>",
+      "single_word": false,
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+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    },
+    "100301": {
+      "content": "<|dummy_37|>",
+      "single_word": false,
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+      "rstrip": true,
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+    },
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+      "content": "<|dummy_38|>",
+      "single_word": false,
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+    },
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+      "content": "<|dummy_40|>",
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+    },
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+      "content": "<|dummy_41|>",
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+      "single_word": false,
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+    },
+    "100323": {
+      "content": "<|dummy_59|>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
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+    },
+    "100324": {
+      "content": "<|dummy_60|>",
+      "single_word": false,
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+      "rstrip": true,
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+    },
+    "100325": {
+      "content": "<|dummy_61|>",
+      "single_word": false,
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+      "rstrip": true,
+      "normalized": false,
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+    },
+    "100326": {
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+      "single_word": false,
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+      "rstrip": true,
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+    "100327": {
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+      "single_word": false,
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+    },
+    "100328": {
+      "content": "<|dummy_64|>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
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+    },
+    "100329": {
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+      "single_word": false,
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+    "100330": {
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+      "single_word": false,
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+      "normalized": false,
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+    "100331": {
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+    "100332": {
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+    "100333": {
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+      "single_word": false,
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+      "rstrip": true,
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+    "100334": {
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+      "single_word": false,
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+      "normalized": false,
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+    "100335": {
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+      "single_word": false,
+      "lstrip": true,
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+      "single_word": false,
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+      "normalized": false,
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+    "100339": {
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+      "rstrip": true,
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+      "special": true
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+    "100340": {
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+      "rstrip": true,
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+    "100341": {
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+      "single_word": false,
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+      "rstrip": true,
+      "normalized": false,
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+    "100342": {
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+      "rstrip": true,
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+    "100343": {
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+      "rstrip": true,
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+    "100344": {
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+      "rstrip": true,
+      "normalized": false,
+      "special": true
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+    "100345": {
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+      "rstrip": true,
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+    "100346": {
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+    "100347": {
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+      "rstrip": true,
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+    "100349": {
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+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    },
+    "100350": {
+      "content": "<think>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    },
+    "100351": {
+      "content": "</think>",
+      "single_word": false,
+      "lstrip": true,
+      "rstrip": true,
+      "normalized": false,
+      "special": true
+    }
+  },
+  "chat_template": "{% if messages[0]['role'] != 'system' %}{{'<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>'}}{% endif %}{% for message in messages %}{% if messages[0]['role'] == 'system' %}{{'<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>'}}{% elif message['role'] == 'user' %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}"
+}