初始化项目，由ModelHub XC社区提供模型
Model: meshllm/olmo-7b-instruct-hf-parity-bf16-mlx Source: Original Platform
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README.md
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README.md
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---
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language: en
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pipeline_tag: text-generation
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tags:
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- mlx
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- meshllm
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- parity
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- same-origin
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- olmo
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library_name: mlx
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license: apache-2.0
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---
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# OLMo-7B-Instruct-hf Parity BF16 MLX
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Same-origin parity artifact derived from `allenai/OLMo-7B-Instruct-hf`.
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This repo contains the high-fidelity `bf16` MLX artifact used for mesh-llm backend parity validation against the corresponding GGUF artifact.
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Accepted local validation status:
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- Exact prompts: MLX now tracks GGUF on the semantic probe set after prompt-render parity fixes, with shared family-level drift on strict one-word canaries
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- Behavior smoke: 0 flagged prompts out of 80 on the MT-Bench-derived harness
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Paired GGUF repo:
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- `meshllm/olmo-7b-instruct-hf-parity-f16-gguf`
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{{ eos_token }}{% for message in messages %}
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{% if message['role'] == 'user' %}
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{{ '<|user|>
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' + message['content'] }}
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{% elif message['role'] == 'assistant' %}
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{{ '<|assistant|>
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' + message['content'] + eos_token }}
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{% endif %}
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{% if loop.last and add_generation_prompt %}
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{{ '<|assistant|>' }}
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{% endif %}
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{% endfor %}
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config.json
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{
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"architectures": [
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"OlmoForCausalLM"
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],
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"attention_bias": false,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_file": "olmo_model.py",
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"model_type": "olmo",
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"use_cache": true,
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"vocab_size": 50304
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}
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.28.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
|
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"model.layers.29.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.self_attn.o_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.30.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.30.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.30.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.31.mlp.down_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.31.mlp.gate_proj.weight": "model-00003-of-00003.safetensors",
|
||||
"model.layers.31.mlp.up_proj.weight": "model-00003-of-00003.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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||||
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|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
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|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
148
olmo_model.py
Normal file
148
olmo_model.py
Normal file
@@ -0,0 +1,148 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Optional
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
|
||||
from mlx_lm.models.activations import swiglu
|
||||
from mlx_lm.models.base import BaseModelArgs, create_attention_mask
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArgs(BaseModelArgs):
|
||||
model_type: str
|
||||
hidden_size: int
|
||||
num_hidden_layers: int
|
||||
intermediate_size: int
|
||||
num_attention_heads: int
|
||||
vocab_size: int
|
||||
num_key_value_heads: int = 0
|
||||
rope_theta: float = 10000.0
|
||||
tie_word_embeddings: bool = False
|
||||
attention_bias: bool = False
|
||||
clip_qkv: Optional[float] = None
|
||||
|
||||
def __post_init__(self):
|
||||
if not self.num_key_value_heads:
|
||||
self.num_key_value_heads = self.num_attention_heads
|
||||
if self.num_key_value_heads != self.num_attention_heads:
|
||||
raise ValueError("Grouped-query attention is not yet implemented for this OLMo staging converter.")
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
dim = args.hidden_size
|
||||
self.n_heads = args.num_attention_heads
|
||||
self.head_dim = dim // self.n_heads
|
||||
self.scale = self.head_dim**-0.5
|
||||
self.clip_qkv = args.clip_qkv
|
||||
|
||||
self.q_proj = nn.Linear(dim, dim, bias=args.attention_bias)
|
||||
self.k_proj = nn.Linear(dim, dim, bias=args.attention_bias)
|
||||
self.v_proj = nn.Linear(dim, dim, bias=args.attention_bias)
|
||||
self.o_proj = nn.Linear(dim, dim, bias=args.attention_bias)
|
||||
self.rope = nn.RoPE(self.head_dim, traditional=False, base=args.rope_theta)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
x: mx.array,
|
||||
mask: Optional[mx.array] = None,
|
||||
cache: Optional[Any] = None,
|
||||
) -> mx.array:
|
||||
bsz, seq_len, _ = x.shape
|
||||
q = self.q_proj(x)
|
||||
k = self.k_proj(x)
|
||||
v = self.v_proj(x)
|
||||
|
||||
if self.clip_qkv is not None:
|
||||
q = mx.clip(q, -self.clip_qkv, self.clip_qkv)
|
||||
k = mx.clip(k, -self.clip_qkv, self.clip_qkv)
|
||||
v = mx.clip(v, -self.clip_qkv, self.clip_qkv)
|
||||
|
||||
q = q.reshape(bsz, seq_len, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
|
||||
k = k.reshape(bsz, seq_len, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
|
||||
v = v.reshape(bsz, seq_len, self.n_heads, self.head_dim).transpose(0, 2, 1, 3)
|
||||
|
||||
if cache is not None:
|
||||
q = self.rope(q, offset=cache.offset)
|
||||
k = self.rope(k, offset=cache.offset)
|
||||
k, v = cache.update_and_fetch(k, v)
|
||||
else:
|
||||
q = self.rope(q)
|
||||
k = self.rope(k)
|
||||
|
||||
out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
|
||||
out = out.transpose(0, 2, 1, 3).reshape(bsz, seq_len, -1)
|
||||
return self.o_proj(out)
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
dim = args.hidden_size
|
||||
hidden = args.intermediate_size
|
||||
self.gate_proj = nn.Linear(dim, hidden, bias=False)
|
||||
self.up_proj = nn.Linear(dim, hidden, bias=False)
|
||||
self.down_proj = nn.Linear(hidden, dim, bias=False)
|
||||
|
||||
def __call__(self, x: mx.array) -> mx.array:
|
||||
return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))
|
||||
|
||||
|
||||
class DecoderLayer(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
dim = args.hidden_size
|
||||
self.self_attn = Attention(args)
|
||||
self.mlp = MLP(args)
|
||||
self.input_layernorm = nn.LayerNorm(dim, affine=False)
|
||||
self.post_attention_layernorm = nn.LayerNorm(dim, affine=False)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
x: mx.array,
|
||||
mask: Optional[mx.array] = None,
|
||||
cache: Optional[Any] = None,
|
||||
) -> mx.array:
|
||||
h = x + self.self_attn(self.input_layernorm(x), mask, cache)
|
||||
return h + self.mlp(self.post_attention_layernorm(h))
|
||||
|
||||
|
||||
class InnerModel(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
|
||||
self.layers = [DecoderLayer(args) for _ in range(args.num_hidden_layers)]
|
||||
self.norm = nn.LayerNorm(args.hidden_size, affine=False)
|
||||
|
||||
def __call__(self, inputs: mx.array, cache=None):
|
||||
h = self.embed_tokens(inputs)
|
||||
if cache is None:
|
||||
cache = [None] * len(self.layers)
|
||||
mask = create_attention_mask(h, cache[0])
|
||||
for layer, layer_cache in zip(self.layers, cache):
|
||||
h = layer(h, mask, layer_cache)
|
||||
h = self.norm(h)
|
||||
return h, cache
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
super().__init__()
|
||||
self.model_type = args.model_type
|
||||
self.model = InnerModel(args)
|
||||
self.args = args
|
||||
self.tie_word_embeddings = args.tie_word_embeddings
|
||||
if not self.tie_word_embeddings:
|
||||
self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
|
||||
|
||||
def __call__(self, inputs: mx.array, cache=None):
|
||||
h, cache = self.model(inputs, cache)
|
||||
if self.tie_word_embeddings:
|
||||
return self.model.embed_tokens.as_linear(h), cache
|
||||
return self.lm_head(h), cache
|
||||
|
||||
@property
|
||||
def layers(self):
|
||||
return self.model.layers
|
||||
250630
tokenizer.json
Normal file
250630
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"is_local": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|padding|>",
|
||||
"tokenizer_class": "GPTNeoXTokenizer",
|
||||
"trim_offsets": true,
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user