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Model: prithivMLmods/Pocket-Llama2-3.2-3B-Instruct Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- trl
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- sft
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- text-generation-inference
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- code
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- Math
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license: llama3.2
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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pipeline_tag: text-generation
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---
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# **Pocket-Llama2-3.2-3B-Instruct**
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> Pocket-Llama2-3.2-3B-Instruct is based on the Llama 3.2 architecture, designed as a lightweight and efficient general-purpose chat assistant. Optimized for fast inference while maintaining strong problem-solving, mathematical reasoning, and scientific capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.
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## **Key Improvements**
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1. **Optimized for General Purpose Chat**: Excels in a wide range of topics, including casual conversation, technical discussions, and knowledge-based queries.
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2. **Strong Math & Science Capabilities**: Provides accurate and structured explanations for mathematical and scientific problems.
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3. **Compact yet Powerful**: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.
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4. **Advanced Reasoning Capabilities**: Excels in algorithmic problem-solving, structured technical explanations, and logical analysis.
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5. **Efficient Memory Utilization**: Reduces computational overhead while maintaining high-quality outputs.
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6. **Focused Output Generation**: Avoids unnecessary token generation, ensuring concise and relevant responses.
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## **Quickstart with transformers**
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Here is a code snippet to load the tokenizer and model using `apply_chat_template` for structured input formatting:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Pocket-Llama2-3.2-3B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Explain the theory of relativity in simple terms."
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messages = [
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{"role": "system", "content": "You are an advanced assistant specialized in science and mathematics."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=6090
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## **Intended Use**
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1. **General Chat & Knowledge-Based Queries**:
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Engages in informative and casual discussions on a wide range of topics.
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2. **Mathematics & Science Problem Solving**:
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Provides accurate calculations and structured explanations for complex problems.
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3. **Technical Documentation & Explanation**:
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Assists in generating well-structured documentation for APIs, scientific concepts, and coding principles.
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4. **Debugging Assistance**:
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Helps identify and correct errors in code snippets.
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5. **Educational Support**:
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Simplifies complex topics for students and learners with clear explanations.
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6. **Structured Data Processing**:
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Generates structured outputs like JSON, XML, and tables for data science applications.
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## **Limitations**
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1. **Hardware Constraints**:
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Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.
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2. **Potential Bias in Responses**:
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Outputs may reflect biases present in training data.
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3. **Limited Creativity**:
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May generate variable results in non-technical, creative tasks.
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4. **No Real-Time Awareness**:
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Lacks access to real-world events beyond its training cutoff.
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5. **Error Propagation in Long Responses**:
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Minor mistakes in early outputs may affect overall coherence in lengthy responses.
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6. **Prompt Sensitivity**:
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The effectiveness of responses depends on well-structured prompts.
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config.json
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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": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.50.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"temperature": 0.6,
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16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
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||||
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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size 17209920
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2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user