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Model: Fu01978/SmollerLM2-360M-Instruct-Pruned Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- HuggingFaceTB/SmolLM2-360M-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- pruned
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- tiny-ml
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- smollm
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- research
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---
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# SmollerLM2-360M-Instruct-Pruned
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A structurally pruned version
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of the [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) model.
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This model was created
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as an experiment in **pruning**.
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## Pruning Methodology
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The model underwent
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**Structured Every-Nth Neuron Pruning**.
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Unlike random dropout
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or unstructured pruning,
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this method maintains
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the dense matrix format
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required by standard hardware
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accelerators.
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- **Target:** Intermediate MLP (Feed-Forward) layers.
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- **Strategy:** Every 20th neuron was removed ($1/20$).
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- **Dimension Shift:** Intermediate size reduced from **2560** to **2432**.
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## Memory Efficiency
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While the original model
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is distributed in FP32,
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this model provides
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an optimization
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that makes it significantly
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more accessible:
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- **Precision Reduction (FP32 → FP16):**
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We converted the weights
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to half-precision,
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instantly cutting the memory footprint
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by **50%**.
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> **Total Savings:** **51.6%** smaller than the original version.
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## Recommended Usage
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> **TECHNICAL NOTE:**
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On CPUs without native FP16 support,
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this model may experience
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a 'tax'
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resulting in slower tokens-per-second
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than the original.
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This model is **RAM-optimized**,
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not necessarily **CPU-Latency optimized**
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in its raw FP16 state.
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To get the best performance,
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it is recommended to
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use it on a GPU
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or via 4-bit/8-bit quantization
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to bypass CPU floating-point limitations.
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You will first need to install
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the `bitsandbytes` library
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in Python.
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(`pip install bitsandbytes`)
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### GPU Loading (Fastest)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Fu01978/SmollerLM2-360M-Instruct-Pruned"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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quantization_config={"load_in_8bit": True},
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device_map="auto"
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)
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```
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### CPU Loading
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If running on lower-end CPUs,
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load the model in **4-bit**
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to ensure the weights fit
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in the L1/L2 cache:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Fu01978/SmollerLM2-360M-Instruct-Pruned"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config={"load_in_4bit": True},
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device_map="auto"
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)
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```
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### Generate
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Use the following snippet
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to chat with the model.
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This uses the chat template.
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```python
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# Define your message(s)
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messages = [
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{"role": "user", "content": "Explain the concept of gravity."}
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]
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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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=150,
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temperature=0.2,
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do_sample=True,
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repetition_penalty=1.1
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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#### Example Output (4-bit Quantized)
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* **Prompt:** "Explain the concept of gravity."
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* **Output:**
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> Gravity is indeed one of the most fundamental concepts in physics and mathematics. It's essentially the "attraction" between two bodies or masses. According to Einstein's theory of general relativity, mass warps space-time around it, creating a gravitational field that attracts other objects with mass. This means that anything having mass has a gravitational pull on other matter, making them feel heavy. For example, when you drop an object, you're not really feeling its weight; rather, you're feeling the gravitational force exerted by the Earth. The actual weight of an object depends on how massive the object itself is, which can be calculated using formulas like F=G x m/r^2 where G is the gravitational constant, **[hit 150 token limit]**
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## Limitations & Bias
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As a pruned version of SmolLM2,
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this model inherits the biases of its parent.
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While the pruning was found to be stable,
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users may encounter slight regressions
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in mathematical reasoning
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compared to the full model.
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chat_template.jinja
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "float16",
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 960,
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"initializer_range": 0.02,
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"intermediate_size": 2432,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 15,
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"num_hidden_layers": 32,
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"num_key_value_heads": 5,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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"rope_parameters": {
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"rope_theta": 100000,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"transformers.js_config": {
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"kv_cache_dtype": {
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"fp16": "float16",
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"q4f16": "float16"
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}
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},
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"transformers_version": "5.0.0",
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"use_cache": true,
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"vocab_size": 49152
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 2,
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"transformers_version": "5.0.0"
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}
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48901
merges.txt
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merges.txt
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7d08e049dadf32903026c14adee671090494521819b4dccbc278582f84515082
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size 700081664
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special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": {
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"content": "<|im_start|>",
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"lstrip": false,
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},
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}
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tokenizer.json
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{
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"add_prefix_space": false,
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"3": {
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"content": "<repo_name>",
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"rstrip": false,
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"single_word": false,
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"4": {
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"content": "<reponame>",
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"lstrip": false,
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"rstrip": false,
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"content": "<jupyter_code>",
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"lstrip": false,
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"content": "<empty_output>",
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": "<|im_start|>",
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"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"model_max_length": 8192,
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"pad_token": "<|im_end|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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vocab.json
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vocab.json
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Reference in New Issue
Block a user