Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +85 -0
- config.json +98 -0
- easydel-model.parameters +3 -0
- generation_config.json +9 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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easydel-model.parameters filter=lfs diff=lfs merge=lfs -text
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README.md
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# BaseTrainer
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## 🚀 Trained With [EasyDeL](https://github.com/erfanzar/EasyDeL)
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EasyDeL is an open-source framework designed to enhance and streamline the training process of machine learning
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models. With a primary focus on Jax, EasyDeL aims to provide convenient and effective solutions for
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training Flax/Jax models on TPU/GPU, for both serving and training purposes.
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## 📦 Installation & Usage
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```python
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from easydel import AutoEasyDeLModelForCausalLM
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from jax import numpy as jnp, lax
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model = AutoEasyDeLModelForCausalLM.from_pretrained(
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f"REPO_ID/BaseTrainer",
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dtype=...,
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param_dtype=...,
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precision=lax.Precision("fastest"),
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auto_shard_model=True,
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)
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```
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## 🔧 Training Configuration
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### Model Details
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- **Architecture**: qwen2
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- **Platform**: TPU
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- **Number of Devices**: 16
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### Training Parameters
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- **Learning Rate**: 5e-05 → 5e-06
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- **Optimizer**: adamw
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- **Scheduler**: cosine
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- **Warmup Steps**: 160
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- **Weight Decay**: 0.02
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- **Loss Config**: LossConfig(
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ignore_index: -100
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label_smoothing: 0.0
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z_loss: 0.0
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loss_normalizing_factor: 'NUM_REAL_TARGET_TOKENS'
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num_labels: None
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problem_type: None
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divide_weight_sum: False
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shift_tokens: True
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break_on_nan: True
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reduction: None
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num_classification_labels: None
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classification_problem_type: None
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)
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### Training Setup
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- **Epochs**: 5
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- **Batch Size**: 16
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- **Sequence Length**: 4096
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- **Dtype**: <class 'jax.numpy.bfloat16'>
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- **Params Dtype**: <class 'jax.numpy.bfloat16'>
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### Advanced Configuration
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- **Gradient Checkpointing**:
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- **Gradient Accumulation Steps**: 1
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- **Max Training Steps**: None
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- **Max Evaluation Steps**: None
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- **Training Duration**: 7H
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### Sharding Configuration
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```python
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# Partition Rules
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( ('model/embed_tokens/embedding', PartitionSpec('tp', ('fsdp', 'sp'))),
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( 'self_attn/(q_proj|k_proj|v_proj)/kernel',
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PartitionSpec(('fsdp', 'sp'), 'tp')),
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('self_attn/o_proj/kernel', PartitionSpec('tp', ('fsdp', 'sp'))),
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('mlp/gate_proj/kernel', PartitionSpec(('fsdp', 'sp'), 'tp')),
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('mlp/down_proj/kernel', PartitionSpec('tp', ('fsdp', 'sp'))),
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('mlp/up_proj/kernel', PartitionSpec(('fsdp', 'sp'), 'tp')),
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('input_layernorm/kernel', PartitionSpec(None,)),
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('post_attention_layernorm/kernel', PartitionSpec(None,)),
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('model/norm/kernel', PartitionSpec(None,)),
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('lm_head/kernel', PartitionSpec(('fsdp', 'sp'), 'tp')),
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('.*', PartitionSpec(None,)))
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```
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---
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*Generated with EasyDeL v0.1.2*
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"attn_mechanism": "flash_attn2",
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"axis_dims": [
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1,
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-1,
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1,
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1
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],
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"axis_names": [
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"dp",
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"fsdp",
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"tp",
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"sp"
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],
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"backend": null,
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"bits": null,
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"blocksize_b": 1,
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"blocksize_k": 128,
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"blocksize_q": 128,
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"bos_token_id": 151643,
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"dcn_axis_dims": null,
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"easy_method": "train",
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"embd_pdrop": 0.0,
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"eos_token_id": 151643,
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"fcm_max_ratio": 0.0,
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"fcm_min_ratio": 0.0,
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"flash_attention_backward_pass_impl": "triton",
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"freq_max_position_embeddings": 4096,
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"gradient_checkpointing": "",
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"hardware_abstraction": false,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"kv_cache_quantization_blocksize": 64,
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"kv_cache_quantization_method": "None",
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"kv_cache_sharding_sequence_axis_name": "sp",
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"mask_max_position_embeddings": 4096,
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"max_position_embeddings": 131072,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"number_rep_kv": 1,
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"pallas_k_block_size": 128,
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"pallas_m_block_size": 128,
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"pallas_n_block_size": 128,
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"partition_axis": [
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[
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"fsdp",
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"dp"
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],
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"sp",
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"sp",
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"tp",
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"sp",
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"tp",
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null,
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null,
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null,
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null,
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"tp",
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"sp",
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null
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],
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"platform": "jax",
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"pretraining_tp": 1,
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"quantization_blocksize": 64,
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"quantization_method": "None",
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"quantization_pattern": ".*",
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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| 80 |
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"rope_theta": 10000.0,
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"scan_attention_layers": false,
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"scan_layers": true,
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"scan_mlp_chunk_size": 1024,
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| 84 |
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"scan_ring_attention": true,
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"sequence_axis_name": "sp",
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| 86 |
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"shard_attention_computation": true,
|
| 87 |
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"sliding_window": null,
|
| 88 |
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"tie_word_embeddings": false,
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| 89 |
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"torch_dtype": "bfloat16",
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| 90 |
+
"transformers_version": "4.50.3",
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| 91 |
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"use_cache": true,
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| 92 |
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"use_mrope": false,
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| 93 |
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"use_scan_mlp": false,
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| 94 |
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"use_sharded_kv_caching": false,
|
| 95 |
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"use_sharding_constraint": false,
|
| 96 |
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"use_sliding_window": false,
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| 97 |
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"vocab_size": 151667
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}
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easydel-model.parameters
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b18e89af5a5df4e1d683c3faf46f080bc41c5a4013daca98184bd5cc0eda3b0
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size 15225580736
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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": 151646,
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"do_sample": true,
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"eos_token_id": 151643,
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"temperature": 0.6,
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"top_p": 0.95,
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"transformers_version": "4.50.3"
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}
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