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Model: rahul7star/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full Source: Original Platform
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rahul7star_Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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337
README.md
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README.md
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---
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base_model: rikunarita/Qwen3-4B-Thinking-2507-Genius-Coder
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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license: apache-2.0
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language:
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- en
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---
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## GGUFF Version
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https://huggingface.co/rahul7star/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full/blob/main/rahul7star_Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full-Q5_K_M.gguf
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## FULL GGUF List
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https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full-GGUF
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# Uploaded finetuned model
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- **Developed by:** rahul7star
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- **License:** apache-2.0
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- **Finetuned from model :** rikunarita/Qwen3-4B-Thinking-2507-Genius-Coder
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This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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## This is base model
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## Code Implementation
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```
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import torch
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import re
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# ---------------- Config ----------------
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MODEL_NAME = "rahul7star/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full"
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# Patch prompt: explicitly tell the model to provide reasoning AND final answer
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PROMPT = (
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"write a python code using pytorch for a simple neural network demo?\n\n"
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"Please provide step-by-step reasoning inside <think> tags, AND then output the final Python code after </think>."
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)
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# ---------------- Load Tokenizer & Model ----------------
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print("Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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print("Loading model...")
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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device_map="auto",
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trust_remote_code=True,
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low_cpu_mem_usage=True
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)
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token = tokenizer.eos_token
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print("✅ Model loaded on device:", model.device)
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# ---------------- Prepare Messages ----------------
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messages = [
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{"role": "user", "content": PROMPT}
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]
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# Apply chat template
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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input_length = inputs.input_ids.shape[-1]
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# ---------------- Generate ----------------
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=True, # required for Thinking models
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temperature=0.9, # encourage variation
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top_p=0.95,
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repetition_penalty=1.05,
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use_cache=True
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)
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# ---------------- Decode ----------------
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# Raw output
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raw_output = tokenizer.decode(outputs[0])
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print("\n--- RAW OUTPUT ---")
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print(raw_output)
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# Only new tokens
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generated_tokens = outputs[0][input_length:]
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sliced_output = tokenizer.decode(generated_tokens, skip_special_tokens=False)
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print("\n--- SLICED OUTPUT ---")
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print(sliced_output)
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# Remove <think> for clarity
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clean_output = re.sub(r"<think>.*?</think>", "", sliced_output, flags=re.DOTALL).strip()
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print("\n--- CLEANED OUTPUT (after <think> removed) ---")
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print(clean_output)
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```
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## Code Output
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```
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--- RAW OUTPUT ---
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<|im_start|>user
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write a python code using pytorch for a simple neural network demo?
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Please provide step-by-step reasoning inside <think> tags, AND then output the final Python code after </think>.<|im_end|>
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<|im_start|>assistant
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<think>
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**Planning the step-by-step**
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I need to create a simple PyTorch neural network. The plan is: import torch and torch.nn; define a neural network with one input layer, one hidden layer (ReLU activation), one output layer, set up random data, train the network using gradient descent and a loss function (MSE), evaluate its performance on new data. Code will be written in a single script following this logic.
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</think>
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```python
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import torch
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import torch.nn as nn
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import numpy as np
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from torch.utils.data import Dataset, DataLoader
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# Define a simple dataset
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class SimpleDataset(Dataset):
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def __init__(self, n_samples=100):
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self.n = n_samples
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# random x between -1 and 1
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self.x = np.linspace(-1, 1, self.n).reshape((-1, 1))
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self.y = 2 * self.x + np.random.normal(0, 0.1, self.n)
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def __len__(self):
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return len(self.x)
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def __getitem__(self, idx):
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return torch.tensor(self.x[idx], dtype=torch.float32), \
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torch.tensor(self.y[idx], dtype=torch.float32)
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# Set up data
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n_train = 60
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n_val = 20
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n_test = 20
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train_data = SimpleDataset(n_samples=n_train)
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val_data = SimpleDataset(n_samples=n_val)
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test_data = SimpleDataset(n_samples=n_test)
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batch_size = 4
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train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
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val_loader = DataLoader(val_data, batch_size=batch_size)
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test_loader = DataLoader(test_data, batch_size=batch_size)
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# Define the neural net
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = nn.Linear(1, 64) # input size=1
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self.relu = nn.ReLU()
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self.linear2 = nn.Linear(64, 32)
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self.linear3 = nn.Linear(32, 1)
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def forward(self, x):
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x = self.linear1(x)
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x = self.relu(x)
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x = self.linear2(x)
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x = self.relu(x)
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x = self.linear3(x)
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return x
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net = Net()
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criterion = nn.MSELoss()
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optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
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--- CLEANED OUTPUT (after <think> removed) ---
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```
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## GGUF Repo
|
||||
https://huggingface.co/spaces/rahul7star/Qwen3-4B-Thinking-2507-Genius-Coder-GGUF-UD-Q4_K_M
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Samle question asked
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```
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explain what are these
|
||||
Qwen3.5-27B-Q2_K.gguf
|
||||
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||||
10.5 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q3_K_M.gguf
|
||||
|
||||
13.5 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q3_K_S.gguf
|
||||
|
||||
12.3 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q4_0.gguf
|
||||
|
||||
15.7 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q4_1.gguf
|
||||
|
||||
17.2 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q4_K_M.gguf
|
||||
|
||||
16.7 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q4_K_S.gguf
|
||||
|
||||
15.8 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q5_K_M.gguf
|
||||
|
||||
19.6 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q5_K_S.gguf
|
||||
|
||||
18.9 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q6_K.gguf
|
||||
|
||||
22.5 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-Q8_0.gguf
|
||||
|
||||
28.6 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-UD-Q4_K_XL.gguf
|
||||
|
||||
16.7 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-UD-Q5_K_XL.gguf
|
||||
|
||||
19.6 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-UD-Q6_K_XL.gguf
|
||||
|
||||
23.1 GB
|
||||
xet
|
||||
Upload folder using huggingface_hub
|
||||
1 day ago
|
||||
Qwen3.5-27B-UD-Q8_K_XL.gguf
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```
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## Response
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```
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<<<reasoning_content_start>>>**Explaining Qwen3.5-27B variants**
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|
||||
These are quantized versions of Qwen3.5-27B, a 27 billion parameter language model, with different precision levels (Q2 to Q8 and "UD") that reduce size and speed. Q2/Q3/K variants offer smaller files (10–12 GB) for fast inference but lower accuracy, while Q5/Q6/Q8/XL variants balance size/accuracy. "UD" indicates mixed-precision quantization for better quality. Each ends with `.gguf`, a format used by tools like llama.cpp. The "K" or "S" suffix denotes quantization strategy (K for K-Means, S for Sparse), and "XL" applies to quantized models with additional optimization. These files enable efficient deployment on limited hardware.<<<reasoning_content_end>>>In the Qwen3.5-27B GitHub repository, those are quantized weights for Qwen3.5-27B, the 27B parameter model from Qwen3.5, packaged in GGUF format for use with tools like llama.cpp.
|
||||
|
||||
- **Q2–Q8**: Quantization precision levels: lower numbers mean less precision, smaller model size, and faster inference.
|
||||
- **K**: K-Means quantization for better accuracy and smoothness.
|
||||
- **S**: Sparse quantization (some weights are zero).
|
||||
- **XL**: Mixed-precision quantization ("UD" = Unstructured, Q4_K_XL, Q5_K_XL, Q6_K_XL, Q8_K_XL) has higher accuracy and lower precision than Q4_0/K, Q5_0/K, Q6_K, Q8_0.
|
||||
- **GGUF**: A standard quantized format used by llama.cpp.
|
||||
|
||||
The numbers (Q2, Q3, Q4, Q5, Q6, Q8) refer to the bits per weight; Q2=2, Q3=3, Q4=4, Q5=5, Q6=6, Q8=8. The model can be run at different precisions (e.g., Q4_K_M gives a smaller file than Q4_K, Q4_S is even smaller but less accurate). The “-UD” suffix indicates a quantization variant with higher accuracy than the basic Qn_K. For example:
|
||||
|
||||
- Q2_K: 10.5 GB – minimal precision, very fast but lowest accuracy
|
||||
- Q4_K: 16–17 GB – good balance for most use cases
|
||||
- Q8_K: 28 GB – near full precision
|
||||
- Q4_K_XL: 16–19 GB – optimized accuracy at Q4 level
|
||||
|
||||
These files can be used with `llama.cpp` or other GGUF-aware frameworks to run Qwen3.5 on devices with limited RAM.
|
||||
<<<reasoning_content_start>>>**Finalizing a clean table for readability**
|
||||
|
||||
The final table provides a clean, structured overview of quantized Qwen3.5-27B GGUF files, showing quantization levels, precision, accuracy trade-offs, and approximate RAM usage per precision level for quick comparison. It clearly distinguishes between Qn_K variants and the “UD”-prefixed variants with improved accuracy, and orders entries by size and accuracy, highlighting the best balance points for different needs.<<<reasoning_content_end>>>Here's a clean, tabular breakdown of the quantized Qwen3.5-27B GGUF files for quick comparison:
|
||||
|
||||
| Quantization Format | Description | Size | RAM Usage | Accuracy Notes | Best For |
|
||||
|------------------------|---------------------------------|----------|---------------|--------------------------------------------|-----------------------------|
|
||||
| `Q2_K` | 2-bit, dense quantization | 10.5 GB | Very low | Lowest accuracy, fastest inference | Minimal RAM, speed focus |
|
||||
| `Q3_K_M` | 3-bit, dense | 13.5 GB | Low | Good speed/accuracy balance | Lightweight use cases |
|
||||
| `Q3_K_S` | 3-bit, sparse | 12.3 GB | Low | Slightly lower accuracy than Q3_K_M | Lower memory than Q3_K_M |
|
||||
| `Q4_0` | 4-bit, dense | 15.7 GB | Medium | Standard precision | General use |
|
||||
| `Q4_K_M` | 4-bit, dense + K-means | 16.7 GB | Medium | Balanced accuracy/speed | Most common trade-off |
|
||||
| `Q4_K_S` | 4-bit, sparse | 15.8 GB | Medium | Slightly lower accuracy than Q4_K_M | Lower RAM than Q4_K_M |
|
||||
| `Q4_K_XL` | 4-bit, dense + mixed precision | 16.7 GB | Medium | Higher accuracy than Q4_0/K, faster than Q4_K_XL | Better quality at Q4 level |
|
||||
| `Q5_K_M` | 5-bit, dense | 19.6 GB | High | High accuracy, larger file | High quality tasks |
|
||||
| `Q5_K_S` | 5-bit, sparse | 18.9 GB | High | Slightly lower accuracy than Q5_K_M | Memory-limited tasks |
|
||||
| `Q5_K_XL` | 5-bit, mixed precision (UD) | 19.6 GB | High | Improved accuracy over Q5_0/K | Quality-focused use |
|
||||
| `Q6_K` | 6-bit, dense | 22.5 GB | High | Near full precision | Balanced quality/size |
|
||||
| `Q6_K_XL` | 6-bit, mixed precision (UD) | 23.1 GB | High | Best Q6 accuracy | High quality |
|
||||
| `Q8_0` | 8-bit, dense | 28.6 GB | Full precision | Near full model precision | Full accuracy |
|
||||
| `Q8_K_XL` | 8-bit, mixed precision (UD) | 28.6 GB | Full precision | Highest accuracy for 8-bit | Best 8-bit quality |
|
||||
|
||||
### Notes
|
||||
- **Qn_K**: `K` = K-means clustering for smoother weights, better accuracy than basic `Qn_0`.
|
||||
- **Qn_S**: Sparse quantization (some zeros), smaller size but lower accuracy than `Qn_K`.
|
||||
- **Qn_K_XL/UD**: "Unstructured" quantization variants (`-UD`) have better accuracy than standard `Qn_K`.
|
||||
- **RAM**: Numbers are rough estimates; actual usage depends on your system and inference setup.
|
||||
- **Trade-offs**: Lower `Qn` = smaller file, faster inference; higher `Qn` = better accuracy, more RAM.
|
||||
|
||||
This format highlights the best options for speed vs. quality trade-offs without technical overload.
|
||||
```
|
||||
28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"</think>": 151668,
|
||||
"</tool_call>": 151658,
|
||||
"</tool_response>": 151666,
|
||||
"<think>": 151667,
|
||||
"<tool_call>": 151657,
|
||||
"<tool_response>": 151665,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
86
chat_template.jinja
Normal file
86
chat_template.jinja
Normal file
@@ -0,0 +1,86 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n<think>\n' }}
|
||||
{%- endif %}
|
||||
73
config.json
Normal file
73
config.json
Normal file
@@ -0,0 +1,73 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"torch_dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 262144,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 5000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_version": "2026.2.1",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b1b1ed988aa0ca31f86d2cf91baa80556ff91110aafb7e1075dca4401c6ecd53
|
||||
size 4987499248
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:804ca7488c21426243b29b08016fb51916584f4031b4604223661aafabf4095d
|
||||
size 3835394792
|
||||
407
model.safetensors.index.json
Normal file
407
model.safetensors.index.json
Normal file
@@ -0,0 +1,407 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 8822848512,
|
||||
"mergekit_version": "0.1.4"
|
||||
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|
||||
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|
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|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user